Does the early bird catch the worm? A large-scale examination of the effects of early participation in online learning

References

• Abdous, M. H. (2019). Influence of satisfaction and preparedness on online students' feelings of anxiety. The Internet and Higher Education, 41, 34–44. https://doi.org/10.1016/j.iheduc.2019.01.001
   Web of Science ®Google Scholar
• Ali, R., & Leeds, E. M. (2009). The impact of face-to-face orientation on online retention: A pilot study. Online Journal of Distance Learning Administration, 12(4).
   Google Scholar
• Austin, J. T., & Vancouver, J. B. (1996). Goal constructs in psychology: Structure, process, and content. Psychological Bulletin, 120(3), 338–375. https://doi.org/10.1037/0033-2909.120.3.338
   Web of Science ®Google Scholar
• Barbour, M. K. (2017). K–12 online learning and school choice: Growth and expansion in the absence of evidence. In R. A. Fox & N. K. Buchanan (Eds.), The Wiley Handbook of school choice (pp. 421–440). Wiley Blackwell. https://doi.org/10.1002/9781119082361.ch29
   Google Scholar
• Black, E. W., Dawson, K., & Priem, J. (2008). Data for free: Using LMS activity logs to measure community in online courses. The Internet and Higher Education, 11(2), 65–70. https://doi.org/10.1016/j.iheduc.2008.03.002
   Web of Science ®Google Scholar
• Bolliger, D. U., & Halupa, C. (2012). Student perceptions of satisfaction and anxiety in an online doctoral program. Distance Education, 33(1), 81–98. https://doi.org/10.1080/01587919.2012.667961
   Web of Science ®Google Scholar
• Calafiore, P., & Damianov, D. S. (2011). The effect of time spent online on student achievement in online economics and finance courses. The Journal of Economic Education, 42(3), 209–223. https://doi.org/10.1080/00220485.2011.581934
   Web of Science ®Google Scholar
• Cho, M. H. (2012). Online student orientation in higher education: A developmental study. Educational Technology Research and Development, 60(6), 1051–1069. https://doi.org/10.1007/s11423-012-9271-4
   Web of Science ®Google Scholar
• Conrad, D. L. (2002). Engagement, excitement, anxiety, and fear: Learners' experiences of starting an online course. The American Journal of Distance Education, 16(4), 205–226. https://doi.org/10.1207/S15389286AJDE1604_2
   Google Scholar
• Corno, L. (1993). The best-laid plans: Modern conceptions of volition and educational research. Educational Researcher, 22(2), 14–22. https://doi.org/10.3102/0013189X022002014
   Google Scholar
• Cosnefroy, L., Fenouillet, F., Mazé, C., & Bonnefoy, B. (2018). On the relationship between the forethought phase of self-regulated learning and self-regulation failure. Issues in Educational Research, 28(2), 329–348. https://www.iier.org.au/iier28/cosnefroy.pdf
   Web of Science ®Google Scholar
• Crossley, S., Paquette, L., Dascalu, M., McNamara, D. S., & Baker, R. S. (2016). Combining click-stream data with NLP tools to better understand MOOC completion. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 6–14). Association for Computing Machinery. https://doi.org/10.1145/2883851.2883931
   Google Scholar
• Davies, J., & Graff, M. (2005). Performance in e‐learning: online participation and student grades. British Journal of Educational Technology, 36(4), 657–663. https://doi.org/10.1111/j.1467-8535.2005.00542.x
   Web of Science ®Google Scholar
• DeBoer, J., Ho, A. D., Stump, G. S., & Breslow, L. (2014). Changing “course”: Reconceptualizing educational variables for massive open online courses. Educational Researcher, 43(2), 74–84. https://doi.org/10.3102/0013189X14523038
   Web of Science ®Google Scholar
• DeVaney, T. A. (2010). Anxiety and attitude of graduate students in on-campus vs. online statistics courses. Journal of Statistics Education, 18(1). https://doi.org/10.1080/10691898.2010.11889472
   PubMedGoogle Scholar
• Dipietro, M. (2010). Virtual school pedagogy: The instructional practices of K-12 virtual school teachers. Journal of Educational Computing Research, 42(3), 327–354. https://doi.org/10.2190/EC.42.3.e
   Web of Science ®Google Scholar
• Doherty, W. (2006). An analysis of multiple factors affecting retention in web-based community college courses. The Internet and Higher Education, 9(4), 245–255. https://doi.org/10.1016/j.iheduc.2006.08.004
   Google Scholar
• Dunn, K. (2014). Why wait? The influence of academic self-regulation, intrinsic motivation, and statistics anxiety on procrastination in online statistics. Innovative Higher Education, 39(1), 33–44. https://doi.org/10.1007/s10755-013-9256-1
   Google Scholar
• Guo, G., & Zhao, H. (2000). Multilevel modeling for binary data. Annual Review of Sociology, 26(1), 441–462. https://doi.org/10.1146/annurev.soc.26.1.441
   Web of Science ®Google Scholar
• Hew, K. F. (2016). Promoting engagement in online courses: What strategies can we learn from three highly rated MOOCS. British Journal of Educational Technology, 47(2), 320–341. https://doi.org/10.1111/bjet.12235
   Web of Science ®Google Scholar
• Hilliard, J., Kear, K., Donelan, H., & Heaney, C. (2020). Students’ experiences of anxiety in an assessed, online, collaborative project. Computers & Education, 143, Article 103675. https://doi.org/10.1016/j.compedu.2019.103675
   Web of Science ®Google Scholar
• Hrastinski, S. (2009). A theory of online learning as online participation. Computers & Education, 52(1), 78–82. https://doi.org/10.1016/j.compedu.2008.06.009
   Web of Science ®Google Scholar
• Huang, E. Y., Lin, S. W., & Huang, T. K. (2012). What type of learning style leads to online participation in the mixed-mode e-learning environment? A study of software usage instruction. Computers & Education, 58(1), 338–349. https://doi.org/10.1016/j.compedu.2011.08.003
   Web of Science ®Google Scholar
• Jo, Y., Maki, K., & Tomar, G. (2018). Time series analysis of clickstream logs from online courses. ArXiv. https://arxiv.org/pdf/1809.04177.pdf
   Google Scholar
• Khalil, M., & Ebner, M. (2017). Clustering patterns of engagement in massive open online courses (MOOCs): The use of learning analytics to reveal student categories. Journal of Computing in Higher Education, 29(1), 114–132. https://doi.org/10.1007/s12528-016-9126-9
   Web of Science ®Google Scholar
• Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. In D. Suthers, K. Verbert, E. Duval, & X. Ochoa (Eds.), Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 170–179). Association for Computing Machinery. https://doi.org/10.1145/2460296.2460330
   Google Scholar
• Lakhal, S., & Khechine, H. (2021). Technological factors of students’ persistence in online courses in higher education: The moderating role of gender, age and prior online course experience. Education and Information Technologies, 1–27. https://doi.org/10.1007/s10639-020-10407-w
   Web of Science ®Google Scholar
• Lawanto, O., Santoso, H. B., Lawanto, K. N., & Goodridge, W. (2017). Self-regulated learning skills and online activities between higher and lower performers on a web-intensive undergraduate engineering course. Journal of Educators Online, 11(3). https://doi.org/10.9743/JEO.2014.3.2
   Google Scholar
• Lee, K. (2017). Rethinking the accessibility of online higher education: A historical review. The Internet and Higher Education, 33, 15–23. https://doi.org/10.1016/j.iheduc.2017.01.001
   Web of Science ®Google Scholar
• Li, C., Xing, W., & Leite, W. (2022). Building socially responsible conversational agents using big data to support online learning: A case with Algebra Nation. British Journal of Educational Technology, 53(4), 776–803. https://doi.org/10.1111/bjet.13227
   Web of Science ®Google Scholar
• Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705. https://doi.org/10.1037/0003-066x.57.9.705
   PubMed Web of Science ®Google Scholar
• Luke, D. A. (2004). Multilevel modeling (Vol. 143). Sage.
   Google Scholar
• Marchand, G. C., & Gutierrez, A. P. (2012). The role of emotion in the learning process: Comparisons between online and face-to-face learning settings. The Internet and Higher Education, 15(3), 150–160. https://doi.org/10.1016/j.iheduc.2011.10.001
   Web of Science ®Google Scholar
• Michinov, N., Brunot, S., Le Bohec, O., Juhel, J., & Delaval, M. (2011). Procrastination, participation, and performance in online learning environments. Computers & Education, 56(1), 243–252. https://doi.org/10.1016/j.compedu.2010.07.025
   Web of Science ®Google Scholar
• Murphy, K. L., & Collins, M. P. (1997). Communication conventions in instructional electronic chats. First Monday, 2(11). https://doi.org/10.5210/fm.v2i11.558
   Google Scholar
• Oliver, R., & Herrington, J. (2001). Teaching and learning online: A beginner's guide to e-learning and e-teaching in higher education. Edith Cowan University.
   Google Scholar
• Paton, R. M., Fluck, A. E., & Scanlan, J. D. (2018). Engagement and retention in VET MOOCs and online courses: A systematic review of literature from 2013–2017. Computers & Education, 125, 191–201. https://doi.org/10.1016/j.compedu.2018.06.013
   Web of Science ®Google Scholar
• Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2005). Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects. Journal of Econometrics, 128(2), 301–323. https://doi.org/10.1016/j.jeconom.2004.08.017
   Web of Science ®Google Scholar
• Rakes, G. C., & Dunn, K. E. (2010). The impact of online graduate students' motivation and self-regulation on academic procrastination. Journal of Interactive Online Learning, 9(1). https://doi.org/10.24059/olj.v24i4.2205
   Web of Science ®Google Scholar
• Rayyan, S., Seaton, D. T., Belcher, J., Pritchard, D. E., & Chuang, I. (2013). Participation and performance in 8.02 x electricity and magnetism: The first physics MOOC from MITx. ArXiv. https://arxiv.org/pdf/1310.3173.pdf
   Google Scholar
• Secker, C. V. (2002). Effects of inquiry-based teacher practices on science excellence and equity. The Journal of Educational Research, 95(3), 151–160. https://doi.org/10.1080/00220670209596585
   Web of Science ®Google Scholar
• Shaw, R. S. (2012). A study of the relationships among learning styles, participation types, and performance in programming language learning supported by online forums. Computers & Education, 58(1), 111–120. https://doi.org/10.1016/j.compedu.2011.08.013
   Web of Science ®Google Scholar
• Song, D., & Kim, D. (2021). Effects of self-regulation scaffolding on online participation and learning outcomes. Journal of Research on Technology in Education, 53(3), 249–263. https://doi.org/10.1080/15391523.2020.1767525
   Web of Science ®Google Scholar
• Sull, E. C. (2020). The beginning connection in an online course: Crucial! Distance Learning, 17(3), 108–111.
   Google Scholar
• Tang, H., & Xing, W. (2022). Massive open online courses for professional certificate programs? Perspectives on professional learners’ longitudinal participation patterns. Australasian Journal of Educational Technology, 38(1), 136–147. https://doi.org/10.14742/ajet.5768
   Web of Science ®Google Scholar
• Wozniak, H., Pizzica, J., & Mahony, M. J. (2012). Design-based research principles for student orientation to online study: Capturing the lessons learnt. Australasian Journal of Educational Technology, 28(5). https://doi.org/10.14742/ajet.823
   Web of Science ®Google Scholar
• Xing, W., Chen, X., Stein, J., & Marcinkowski, M. (2016). Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization. Computers in human behavior, 58, 119–129. https://doi.org/10.1016/j.chb.2015.12.007
   Web of Science ®Google Scholar
• Xing, W., & Du, D. (2019). Dropout prediction in MOOCs: Using deep learning for personalized intervention. Journal of Educational Computing Research, 57(3), 547–570. https://doi.org/10.1177/0735633118757015
   Web of Science ®Google Scholar
• Xing, W., Li, C., Chen, G., Huang, X., Chao, J., Massicotte, J., & Xie, C. (2021). Automatic assessment of students’ engineering design performance using a Bayesian network model. Journal of Educational Computing Research, 59(2), 230–256. https://doi.org/10.1177/0735633120960422
   Web of Science ®Google Scholar
• Xing, W., Tang, H., & Pei, B. (2019). Beyond positive and negative emotions: Looking into the role of achievement emotions in discussion forums of MOOCs. The Internet and Higher Education, 43, 100690. https://doi.org/10.1016/j.iheduc.2019.100690
   Web of Science ®Google Scholar
• Xu, J., Du, J., & Fan, X. (2013). “Finding our time”: Predicting students' time management in online collaborative groupwork. Computers & Education, 69, 139–147. https://doi.org/10.1016/j.compedu.2013.07.012
   Web of Science ®Google Scholar
• Yang, D., Baldwin, S., & Snelson, C. (2017). Persistence factors revealed: Students’ reflections on completing a fully online program. Distance Education, 38(1), 23–36. https://doi.org/10.1080/01587919.2017.1299561
   Web of Science ®Google Scholar
• Zacharis, N. Z. (2015). A multivariate approach to predicting student outcomes in web-enabled blended learning courses. The Internet and Higher Education, 27, 44–53. https://doi.org/10.1016/j.iheduc.2015.05.002
   Web of Science ®Google Scholar
• Zembylas, M. (2008). Adult learners’ emotions in online learning. Distance Education, 29(1), 71–87. https://doi.org/10.1080/01587910802004852
   Web of Science ®Google Scholar
• Zimmerman, B. J. (2000). Handbook of self-regulation. Academic Press. https://doi.org/10.1016/B978-0-12-109890-2.X5027-6
   Google Scholar
• Zimmerman, B. J. (2008). Goal setting: a key proactive source of academic self-regulation. In D. H. Schunk & B. J. Zimmerman (Eds.), Motivation and self-regulated learning: Theory, research, and applications (pp. 267–295). Lawrence Erlbaum Associates Publishers.
   Google Scholar